Energy-Efficiency-Based Resource Allocation Framework for Cognitive Radio Networks With FBMC/OFDM

Denis, Juwendo; Pischella, Mylène; Ruyet, Didier Le

doi:10.1109/tvt.2016.2622563

Cited by 32 publications

(25 citation statements)

References 46 publications

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“…(1) / * initialization * / (2) Generate topology of primary systems; (3) Generate SIDs appearance probability matrix randomly; (4) Set the values of P r , P t , G t , G r , P sb , P pu , P su , and λ; (5) Determine the PSO parameters: c1, c2, w, npop, iteration number; (6) Set particle(p).best.U(x) � 0 globalbest.U(x) � 0; (7) Generate particle (SIGSs positions and channels) positions and velocities using Equations (13) and (14); (8) Obtain the SIGS position and matched channel; (9) Set iteration � 0; (10) / * calculate every particles utility function * / (11) while (iteration ≤ predetermined maximum iteration number) (12) { (13) for p population size, p � 1 to npop (14) { (15) for m SIGS, m � 1 to N s (16) { (17) Calculate the S_ESINR j k,i and P_WSINR j l based on particle positions; (18) Check the constraint using Equations (11) and (12); (19) if…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Taking into consideration the energy limitations, many papers have studied resource allocation schemes aimed at saving energy. For example, Denis et al [15] investigated the problem of SUs' energy efficiency maximization under secondary total power and primary interference constraints. In CRNs, utilizing resources without subjecting PUs to harmful interference is a crucial problem; this means that any method used to assign the resources must guarantee that PU transmissions are always prioritized.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Cognitive Radio Secondary Information Gathering Station Positioning and Operating Channel Selection for IoT Sensor Networks

Jinyi

Yang

Yoo

2018

Mobile Information Systems

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e Internet of ings (IoT) is the interconnection of different objects through the internet using different communication technologies. e objects are equipped with sensors and communications modules. e cognitive radio network is a key technique for the IoT and can effectively address spectrum-related issues for IoT applications. In our paper, a novel method for IoT sensor networks is proposed to obtain the optimal positions of secondary information gathering stations (SIGSs) and to select the optimal operating channel. Our objective is to maximize secondary system capacity while protecting the primary system. In addition, we propose an appearance probability matrix for secondary IoT devices (SIDs) to maximize the supportable number of SIDs that can be installed in a car, in wearable devices, or for other monitoring devices, based on optimal deployment and probability. We derive fitness functions based on the above objectives and also consider signal to interference-plus-noise ratio (SINR) and position constraints. e particle swarm optimization (PSO) technique is used to find the best position and operating channel for the SIGSs. In a simulation study, the performance of the proposed method is evaluated and compared with a random resources allocation algorithm (parts of this paper were presented at the ICTC2017 conference (Wen et al., 2017)).

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of Cognitive Radio Secondary Information Gathering Station Positioning and Operating Channel Selection for IoT Sensor Networks

Jinyi

Yang

Yoo

2018

Mobile Information Systems

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“…From (B.3) and (B.4), we can observe that the feasible solutions of (22) are also suitable for (16). According to (19), we also obtain f 2 (P n+1 p,i ,P n+1 s,i ,P n+1 z,i ) ≤ f 2 (P n p,i ,P n s,i ,P n z,i ) Then, following the iterative procedure in (22), we arrive at I i=1 f 1 (P n+1 p,i ,P n+1 s,i ,P n+1 z,i ) − f 2 (P n p,i ,P n s,i ,P n z,i ) − b i (P n+1 p,i −P n p,i ) (b iP n p,i + σ 2 c,i )ln2 From (B.7), we can observe that the proposed iterative procedure is monotonically non-decreasing with the increasing of iterative numbers. In addition, by employing the transmit power constraints of PBS and CBS, i.e., I i=1 P p,i ≤ P total PBS and I i=1 (P s,i + P z,i ) ≤ P total CBS , the upper bound of the objective function can be given by…”

Section: (B4)mentioning

confidence: 86%

“…Based on the given tolerance ε, we can give the iterations as ∆f N0 ln 2 . Given 3I scalar variables in problem (22), so we need at most O((3I) 3.5 log(1/ε)) calculations at each inner iteration [42]. Finally, the overall computational complexity of the proposed scheme can be roughly written as Step 1: Initialize the maximum number of iterations mmax, nmax and the maximum tolerance ε.…”

Section: Two-tier Iterative Algorithm For Icsi-seemmentioning

confidence: 99%

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Secrecy Energy Efficiency Optimization for Artificial Noise Aided Physical-Layer Security in OFDM-Based Cognitive Radio Networks

Jiang

Zou

Ouyang

et al. 2018

IEEE Trans. Veh. Technol.

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In this paper, we investigate the power allocation of primary base station (PBS) and cognitive base station (CBS) across different orthogonal frequency division multiplexing (OFDM) subcarriers for energy-efficient secure downlink communication in OFDM-based cognitive radio networks (CRNs) with the existence of an eavesdropper having multiple antennas. For the sake of defending against eavesdropping, artificial noise is used to confuse the eavesdropper at the cost of extra power consumption. For the purpose of improving the energy efficiency (EE) of secure communications, we propose a secrecy energy efficiency maximization (SEEM) scheme by exploiting the instantaneous channel state information (ICSI) of the eavesdropper, called ICSI based SEEM (ICSI-SEEM) scheme with a given total transmit power budget for different OFDM subcarriers of both PBS and CBS while guaranteeing a certain secrecy rate (SR) for a cognitive user, where a primary user' SR is also taken into consideration for limiting the interference in CRNs at each subcarrier. As for the case when the eavesdropper's ICSI is unknown, we also propose an SEEM scheme through using the statistical CSI (SCSI) of the eavesdropper, namely SCSI based SEEM (SCSI-SEEM) scheme. Since the ICSI-SEEM and SCSI-SEEM problems are fractional and non-convex, we first transform them into equivalent subtractive problems, and then achieve approximate convex problems through employing the difference of two-convex functions approximation method. Finally, new two-tier power allocation algorithms are proposed to achieve ε-optimal solutions of our formulated ICSI-SEEM and SCSI-SEEM problems. Simulation results illustrate that the ICSI-SEEM has a better secrecy energy efficiency (SEE) performance than SCSI-SEEM, and moreover, the proposed ICSI-SEEM and SCSI-SEEM schemes outperform conventional SR maximization and EE maximization approaches in terms of their SEE performance.

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Novel Heuristic Subcarrier Allocation for Spectral: Energy Efficiency Tradeoff Improvement in Underlay Cognitive Radio Network

Sasikumar

Jayakumari

2023

Wireless Pers Commun

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Energy-Efficiency-Based Resource Allocation Framework for Cognitive Radio Networks With FBMC/OFDM

Cited by 32 publications

References 46 publications

Optimization of Cognitive Radio Secondary Information Gathering Station Positioning and Operating Channel Selection for IoT Sensor Networks

Optimization of Cognitive Radio Secondary Information Gathering Station Positioning and Operating Channel Selection for IoT Sensor Networks

Secrecy Energy Efficiency Optimization for Artificial Noise Aided Physical-Layer Security in OFDM-Based Cognitive Radio Networks

Novel Heuristic Subcarrier Allocation for Spectral: Energy Efficiency Tradeoff Improvement in Underlay Cognitive Radio Network

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